Syllabus

Instructor: George Hagstrom, Ph.D. Class Meetup: Monday 6:45-7:45 Eastern Office Hours: By appointment Email: george.hagstrom@cuny.edu

Description

This course provides a foundational understanding of machine learning techniques, covering both supervised and unsupervised methods, including regression, classification, clustering, and dimensionality reduction. Students will gain hands-on experience with algorithms such as Generalized Linear Models, decision trees, and neural networks, as well as deep learning and pre-trained models. The course also explores model evaluation, regularization, and the bias-variance tradeoff, with a focus on ethics, bias, fairness, and considerations involving production deployment. By the end, students will be prepared to apply machine learning to real-world problems and assess their societal implications.

Students will complete a project to create a production Machine Learning application.

Course Learning Outcomes

By the end of the course, students should be able to:

  • Understand how to formulate major statistical and machine learning algorithms as optimization problems
  • Learn how to recognize and solve least squares, linear programming, and convex optimization problems.
  • Learn how to represent convex optimization problems in the CVX package
  • Understand the basics of algorithmic complexity theory and use it to understand how quickly different algorithms will converge to the solution of an optimization problem
  • Implement stochastic gradient descent for neural networks and other non-convex problems, understand trade-offs in algorithm design

Program Learning Outcomes

By the end of the course, students should be able to:

  • Understand Data Science Foundations: Demonstrate foundational knowledge of machine learning principles.
  • Apply Machine Learning Methods: Apply statistical learning and machine learning to analyze and interpret real world datasets. Evaluate and enhance machine learning models for accuracy and generalization.
  • Develop Computational Solutions: Implement machine learning algorithms and workflows using python. Develop and deploy end-to-end machine learning applications.
  • Solve Real-World Problems: Address real-world challenges using supervised, unsupervised, and advanced learning approaches.
  • Communicate Insights: Communicate the results and insights of machine learning based analyses to both technical and non-technical stakeholders
  • Adopt Ethical AI Practices: Integrate ethical principles, fairness, and transparency into AI systems. Understand the ethical challenges of AI, including bias, fairness, transparency, and the responsible deployment of machine learning models.

Course Learning Outcomes

  • Understand Machine Learning Foundations: Explain foundational concepts, types of learning, and machine learning pipelines.
  • Implement Linear and Logistic Models: Apply and evaluate linear and logistic regression models in R.
  • Use Classification Techniques: Employ classification methods such as discriminant analysis, kNN, and decision trees.
  • Analyze and Optimize Models: Explore bias-variance tradeoff and use regularization techniques to improve models.
  • Apply Ensemble Methods: Use boosting, bagging, random-forests, and BART to solve enhance the use of decision trees and other methods.
  • Causal Inference: Learn the difference between prediction and causal inference and how to design basic causal models.
  • Model Interpretation: Understand how to interpret and present the results machine learning analyses.
  • Perform Unsupervised Learning: Conduct clustering and dimensionality reduction using methods like PCA.
  • Understand Neural Network Basics: Explore perceptrons, activation functions, and backpropagation in neural networks.
  • Learn Advanced Deep Learning Techniques: Apply CNNs, RNNs, and transfer learning in practical tasks. Understand hyperparameter selection in stochastic gradient descent for deep networks- Apply Pretrained Models: Understand how to access, use, and modify pre-trained models based on transformers.
  • Evaluate AI Ethics and Fairness: Critically assess ethical considerations, bias, and responsible AI deployment.

Grading

Grade Distribution

Quality of Performance Letter Grade Range % GPA
Excellent - work is of exceptional quality A 93 - 100 4
Excellent A- 90 - 92.9 3.7
Good - work is above average B+ 87 - 89.9 3.3
Satisfactory B 83 - 86.9 3
Below Average B- 80 - 82.9 2.7
Poor C+ 77 - 79.9 2.3
Poor C 70 - 76.9 2
Failure F < 70 0

How This Course Works

This course is conducted entirely online. Each week, you will have various resources made available, including weekly readings from the textbooks and occasionally additional readings provided by the instructor. A homework assignment will be due every other week, see the schedule for details). There will also be a final project required. You are expected to complete all assignments by their due dates.

You are expected to attend or watch every Meetup. I highly recommend attending the Meetups live if possible but understand that may not be possible for everyone. Recordings will be made available by the next morning on the Schedule page. In addition to highlighting key concepts from each learning module, some topics will be discussed that are not in the textbook. Moreover, we regularly make announcements in the Meetups that will be important to being successful in this course.

Textbooks and Course Materials

This course makes use of several textbooks. I have attempted when possible to choose resources which are freely available.

  1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor. Introduction to Statistical Learning with Applications in Python

  2. Alex Gold DevOps for Data Science

  3. Matheus Facure Alves. Causal Inference for The Brave and True.

  4. Vincent Arel-Bundock. Model to Meaning

  5. Solon Barocas, Moritz Haidt, and Arvind Narayanan. Fairness and Machine Learning: Limitations and Opportunities.

Optional

  1. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Elements of Statistical Learning

  2. Yasser Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin. Learning from Data

  3. Aurelien Geron. Hands on Machine Learning with Sci-Kit Learn and PyTorch.

  4. Kevin Murphy. Probabilistic Machine Learning.

  5. Andriy Burkov. The 100 Page Machine Learning Book

Accessibility and Accommodations

The CUNY School of Professional Studies is firmly committed to making higher education accessible to students with disabilities by removing architectural barriers and providing programs and support services necessary for them to benefit from the instruction and resources of the University. Early planning is essential for many of the resources and accommodations provided. Please see: http://sps.cuny.edu/student_services/disabilityservices.html

Online Etiquette and Anti-Harassment Policy

The University strictly prohibits the use of University online resources or facilities, including Brightspace, for the purpose of harassment of any individual or for the posting of any material that is scandalous, libelous, offensive or otherwise against the University’s policies. Please see: http://media.sps.cuny.edu/filestore/8/4/9_d018dae29d76f89/849_3c7d075b32c268e.pdf

Academic Integrity

Academic dishonesty is unacceptable and will not be tolerated. Cheating, forgery, plagiarism and collusion in dishonest acts undermine the educational mission of the City University of New York and the students’ personal and intellectual growth. Please see: http://media.sps.cuny.edu/filestore/8/3/9_dea303d5822ab91/839_1753cee9c9d90e9.pdf

Student Support Services

If you need any additional help, please visit Student Support Services: http://sps.cuny.edu/student_resources/